1. Field of the Invention
The present invention relates to recommendation systems. More particularly, the present invention is related to computer-implemented personalized recommendation methods and systems used for lifestyle items.
2. Related Art
Evaluating lifestyle items is a highly subjective process. People often consider intangible style elements, branding, and public perception of themselves as well as the items in question in determining whether to make a purchase decision. This becomes complicated, in that people look to understand the style of a product, and then, how the product and its style relate to them individually. Since it is difficult to assign a quantitative estimate to style, a straightforward, non data-intensive approach to matching users with unique items has never been accomplished.
Currently, popular pre-existing recommendation systems involve singular value decomposition (SVD), collaborative filtering, attribute based tagging, and data mining algorithms. For singular value decomposition and collaborative filtering, millions of data are collected and then “factors” are mathematically determined between points in attempt to predict future data sets. In the case of Netflix, each user rates individual movies on a scale of 0-5 and then an algorithm attempts to derive how future movies will also be rated. Some of the data employed in this process are movie information and groupings, including genre, date, actors, user queue histories, and a set of user ratings from rented movies.
From this data, an algorithm can match users based on their rating history and the statistical likelihood that their ratings will correlate with those of similar users. Note that the “factors” that link users to ratings are not necessarily predicted in advance; rather the SVD approach determines the statistical significance of causal links after a considerable data set already exists. Despite the success of this system, there are some limitations: (1) it requires a very large initial dataset of user ratings (Netflix uses more than 100 million); (2) prior to obtaining relevant results, users are required to first create a baseline by rating several films; and (3) recommendations are based only on the rated items—users rate movies, and then are recommended movies.
These restrictions are also prevalent in many of the advanced data mining techniques comparing browser cookies, query results, purchasing behavior, and other rating systems. A popular alternative is an attribute based tagging system used by Pandora as part of the Music Genome Project. In this system, songs are manually tagged with over 400 distinct musical attributes such as vocal harmony, pitch, lyrics, and instruments. Users can then choose some of their favorite songs and an algorithm will map user preferences against the database of cataloged songs. As with the aforementioned recommendation systems, this approach also presents some limitations: (1) the user is required to rate a variety of music prior to generating relevant results; (2) the songs are tagged by attributes explicitly related to music, and not potential users (listeners); (3) a sufficient number of attributes for each song is required to provide beneficial results; and (4) recommendations are confined to the types of items rated.
Consequently, a need exists for a flexible personalized recommendation system that does not require an expansive data set to develop reliable recommendations for each individual user of the system.